TITLE:
Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps
AUTHORS:
John W. Makokha, Jared O. Odhiambo
KEYWORDS:
Aerosol Optical Depth, Ångström Exponent, Neural Network, Satellite Spectral Imaging, Precipitation Rate, East African Atmosphere
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.6 No.6,
June
25,
2018
ABSTRACT: Neural network analysis based on Growing Hierarchical Self-Organizing Map
(GHSOM) is used to examine Spatial-Temporal characteristics in Aerosol
Optical Depth (AOD), Ångström Exponent (ÅE) and Precipitation Rate (PR)
over selected East African sites from 2000 to 2014. The selected sites of study
are Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0° - 0.6°S; 35.1°E -
35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala
(0°N, 32.1°E). GHSOM analysis reveals a marked spatial variability in AOD
and ÅE that is associated to changing PR, urban heat islands, diffusion, direct
emission, hygroscopic growth and their scavenging from the atmosphere specific
to each site. Furthermore, spatial variability in AOD, ÅE and PR is distinct
since each variable corresponds to a unique level of classification. On the
other hand, GHSOM algorithm efficiently discriminated by means of clustering
between AOD, ÅE and PR during Long and Short rain spells and dry spell
over each variable emphasizing their temporal evolution. The utilization of
GHSOM therefore confirms the fact that regional aerosol characteristics are
highly variable be it spatially or temporally and as well modulated by PR received
over each variable.